It is to be noted that there is a great practical difference between "information" and "know-how" for the purpose of the present invention. "Information" is the collection of a group of facts whether related or unrelated on a subject or field. Whereas "know-how" is that "information" put into context relative to specific applications. That is "know-how", herein, also contains that which is added to identify the relationships between all the items of "information" and relate these that are important and discard those that are irrelevant for a specific application.
It is known to use Expert Systems (ES's) to attempt to store know-how. To set up an ES an expert in a particular field must develop "rules" concerning the field. The rules may be considered as a collection of situation-action rules, each of which captures a single inference or action for a particular type of situation.
A user of an ES asks questions either in free form text or in a special language. The ES answers such questions from its store of rules. Generally the ES will produce an answer to the question however there is no guide to the validity of the answer. That is the ES does not know the boundaries of its own know-how.
Accordingly, when using the ES, specialists in the field of the know-how, of a skill level approaching that of the expert, are required to provide context for the extracted information to determine the validity thereof and hence provide valuable know-how. Naive users or users inexperienced in the field cannot provide such contexts and the validity of the resultant know-how provided by the ES to them is therefore doubtful and approaches mere information.
Also, once an ES is set up validation of its rules require an enormous investment of time and effort since all possible questions must be put to the ES to determine the correctness of the answers.
Further, to set up an ES requires an expert in ES's. This adds to the time and cost involved.
Still further, to develop each rule in the ES is very time consuming. It may take a day to develop one rule. Hence, ES's tend to be applied to very narrow fields of expertise, which add to the know-how boundary problem stated above. Still further, ES's have great difficulty in associating related information in their store of know-how. Addition of new know-how to an ES is a massive task; increasing with each further addition.
Basically, ES's lack the common sense that would define its know-how boundaries. Such common sense in an ES could only be achieved by a very broad know-how base, which by its size increases the risk of error and difficulty of guarding against this.
The ultimate problem in using an ES is to know the correct question to ask to get the correct answer to a given problem. That is the skill of the user is critical to the success of using the ES.